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https://hdl.handle.net/20.500.14279/9634
Τίτλος: | Tensor-Cuts: A simultaneous multi-type feature extractor and classifier and its application to road extraction from satellite images | Συγγραφείς: | Poullis, Charalambos | Major Field of Science: | Social Sciences | Field Category: | Media and Communications | Λέξεις-κλειδιά: | Feature classification;Feature extraction;Graph-Cuts;Road extraction;Tensor | Ημερομηνία Έκδοσης: | Σεπ-2014 | Πηγή: | ISPRS Journal of Photogrammetry and Remote Sensing, 2014, vol. 95, pp. 93-108 | Volume: | 95 | Start page: | 93 | End page: | 108 | Περιοδικό: | ISPRS Journal of Photogrammetry and Remote Sensing | Περίληψη: | Many different algorithms have been proposed for the extraction of features with a range of applications. In this work, we present Tensor-Cuts: a novel framework for feature extraction and classification from images which results in the simultaneous extraction and classification of multiple feature types (surfaces, curves and joints). The proposed framework combines the strengths of tensor encoding, feature extraction using Gabor Jets, global optimization using Graph-Cuts, and is unsupervised and requires no thresholds. We present the application of the proposed framework in the context of road extraction from satellite images, since its characteristics makes it an ideal candidate for use in remote sensing applications where the input data varies widely. We have extensively tested the proposed framework and present the results of its application to road extraction from satellite images. | URI: | https://hdl.handle.net/20.500.14279/9634 | ISSN: | 09242716 | DOI: | 10.1016/j.isprsjprs.2014.06.006 | Rights: | © Elsevier | Type: | Article | Affiliation: | Cyprus University of Technology | Publication Type: | Peer Reviewed |
Εμφανίζεται στις συλλογές: | Άρθρα/Articles |
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